Pre-Submission Review for Health Economics Papers

Health economics reviewers often ask:

• what decision problem is the analysis answering?
• whose perspective is used?
• are comparators appropriate and realistic?
• is the model structure justified?
• are costs, utilities, transition probabilities, and clinical inputs traceable?
• is the time horizon long enough for the outcome?
• are deterministic and probabilistic sensitivity analyses adequate?
• are missing data, uncertainty, and heterogeneity handled transparently?
• does the manuscript follow CHEERS 2022 where relevant?
• does the journal target match HEOR, clinical, policy, or methods readership?

The model has to be interpretable as a decision tool, not only as an analysis exercise.

In our pre-submission review work, health economics manuscripts most often fail when the conclusion is stronger than the model transparency allows.

Perspective blur: the paper mixes payer, societal, provider, or patient perspectives without clearly separating inputs and conclusions.

Comparator mismatch: the analysis compares against a convenient control rather than the actual decision alternative.

Uncertainty weakness: deterministic sensitivity checks are included, but probabilistic uncertainty is missing or underdeveloped.

Input opacity: costs, utilities, transition probabilities, or resource-use assumptions are not traceable enough for review.

Decision overclaim: the conclusion sounds like an adoption recommendation even when uncertainty or setting limits are large.

A useful review should identify whether the model is decision-ready or merely technically complete.

The CHEERS 2022 statement says health economic evaluations are comparative analyses of alternative courses of action in terms of costs and consequences, and that the reporting guidance is intended for any form of health economic evaluation. It replaced earlier CHEERS guidance and reflects new methods, broader contexts, and stakeholder involvement.

Value in Health author guidance recommends following CHEERS 2022 for economic evaluations and submitting a completed CHEERS checklist as supplementary material. It also points authors to other relevant reporting standards such as PRISMA and CONSORT depending on study design.

Those signals make health economics readiness a reporting, modeling, and decision-logic problem.

Send the manuscript, target journal, model diagram, parameter table, data sources, cost inputs, utility inputs, time horizon rationale, perspective statement, sensitivity analysis outputs, probabilistic sensitivity analysis files, CHEERS checklist, PRISMA or CONSORT materials if relevant, and prior reviewer comments if available.

If the analysis supports a payer, HTA, hospital, or policy decision, include the decision context and any local constraints.

A useful health economics pre-submission review should include:

• decision-problem verdict
• model-structure critique
• cost, utility, and input-traceability check
• uncertainty and sensitivity-analysis review
• CHEERS reporting check
• decision-language and generalizability review
• journal-lane recommendation
• submit, revise, retarget, or diagnose deeper call

The review should not only say "add sensitivity analysis." It should name the uncertainty that could change the decision.

Before submission, authors often need to:

• state the decision problem more precisely
• separate payer, societal, provider, and patient perspectives
• justify comparators and time horizon
• make parameter sources and assumptions auditable
• add probabilistic sensitivity analysis
• distinguish base-case, scenario, and subgroup claims
• complete the CHEERS checklist
• retarget from a clinical journal to HEOR, Value in Health, policy, or methods venues when the paper's value is mainly economic

These fixes help reviewers see whether the model supports the claim.

A cost-effectiveness model needs decision context, comparators, model structure, uncertainty, and ICER interpretation. A budget-impact analysis needs payer or system perspective, adoption assumptions, population size, and time horizon discipline. A trial-based economic evaluation needs missing-data handling, resource-use collection, utility measurement, and sensitivity analyses. A preference study needs instrument quality, sampling, and interpretation restraint. An HEOR real-world study needs data provenance, confounding, subgroup discipline, and decision relevance.

The AI manuscript review can flag whether the blocking risk is CHEERS reporting, model assumptions, uncertainty, or journal fit.

Use this page when the manuscript's submission risk depends on healthcare costs, consequences, economic evaluation, HTA, budget impact, patient-reported outcomes, preference data, or decision modeling. Use economics review for broader economics claims. Use public health review when the main question is population relevance, equity, implementation, or policy without a formal economic-evaluation frame.

That distinction keeps the page focused on the health economics buyer's actual problem.

Do not submit a health economics paper if the main conclusion would change under plausible assumptions that are not tested. Sensitivity analysis is not decoration here. It is part of the claim.

Also pause if the decision perspective is unclear. A result can look cost-effective from one perspective and weak from another. Reviewers need to know whose decision the paper is written for.

For model-based work, pause again if the structure is hard to explain without walking someone through a spreadsheet. A reviewer should be able to understand the health states, transition rules, cycle length, assumptions, and outputs from the manuscript itself. If the model is only clear to the analyst who built it, the submission package is not ready.

For trial-based work, pause if missingness or resource-use collection is treated as a minor limitation. Economic conclusions can change when follow-up, costs, visits, medication use, or quality-of-life measures are incomplete. The manuscript should show how those gaps were handled before it asks readers to trust the result.